Estimation of dynamic neural activity using a Kalman filter approach based on physiological models

Author

Giraldo, E. ; den Dekker, A.J. ; Castellanos-Dominguez, G.

Author_Institution

Fac. of Electr. & Electron. Eng., Phys. & Comput. Sci., Technol. Univ. of Pereira, Pereira, Colombia

fYear

2010

fDate

Aug. 31 2010-Sept. 4 2010

Firstpage

2914

Lastpage

2917

Abstract

This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter´s performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.

Keywords

Kalman filters; electroencephalography; medical signal processing; neurophysiology; physiological models; EEG; Kalman filter; dynamic neural activity estimation; linear model; nonlinear model; physiological models; spatial dynamics; temporal dynamics; time invariant parameters; time-varying parameters; Brain models; Computational modeling; Electroencephalography; Estimation; Inverse problems; Kalman filters; Algorithms; Brain Mapping; Computer Simulation; Electroencephalography; Hemodynamics; Humans; Linear Models; Magnetic Resonance Imaging; Models, Statistical; Monte Carlo Method; Normal Distribution; Reproducibility of Results; Time Factors;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Conference_Location

Buenos Aires

ISSN

1557-170X

Print_ISBN

978-1-4244-4123-5

Type

conf

DOI

10.1109/IEMBS.2010.5626281

Filename

5626281